Comparison of STOP-Bang and STOP-Bag questionnaires in stratifying risk of obstructive sleep apnea
Bibliographic record
Abstract
RATIONALE AND OBJECTIVE: The snoring, tiredness, observed apnea, high BP, BMI, age, neck circumference, and male gender (STOP-Bang) questionnaire is used widely to screen individuals at high risk of OSA. The objective of the study is to examine the diagnostic performance of the STOP-Bang questionnaire versus the STOP-Bag (without neck circumference) questionnaire. We hypothesized that the diagnostic performance of the STOP-Bang questionnaire would be higher than STOP-Bag questionnaire.METHODS: A retrospective study was conducted that included patients from two preoperative clinics. All participants completed the STOP-Bang questionnaire and underwent polysomnography (PSG). The diagnostic parameters were calculated for the STOP-Bang questionnaire and the STOP-Bag questionnaire versus polysomnography as the reference standard.RESULTS: There were 203 patients with mean age of 57 ± 13 years and 51% were male. The STOP-Bang questionnaire had a significantly higher area under receiver operating curve than the STOP-Bag questionnaire (0.782 vs 0.758, P < 0.05) in detection of mild to severe OSA in surgical patients. Similarly, the STOP-Bang questionnaire had significantly higher sensitivity when compared to the STOP-Bag questionnaire (85.5% vs 81.3%, P < 0.05). The area under the curve for screening moderate-to-severe and severe OSA was not significantly different for STOP-Bang and STOP-Bag questionnaires.CONCLUSION: Compared to the STOP-Bag questionnaire, the STOP-Bang questionnaire has higher diagnostic performance in predicting all OSA, but the 2 questionnaires were similar for moderate-to-severe and severe OSA. The STOP-Bag questionnaire can be used for screening OSA when neck circumference measurement is not feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".